Bayesian estimation of state space models using moment conditions

A. Ronald Gallant, Raffaella Giacomini () and Giuseppe Ragusa ()

Journal of Econometrics, 2017, vol. 201, issue 2, 198-211

Abstract: We consider Bayesian estimation of state space models when the measurement density is not available but estimating equations for the parameters of the measurement density are available from moment conditions. The most common applications are partial equilibrium models involving moment conditions that depend on dynamic latent variables (e.g., time–varying parameters, stochastic volatility) and dynamic general equilibrium models when moment equations from the first order conditions are available but computing an accurate approximation to the measurement density is difficult.

Keywords: State space models; Bayesian estimation; Moment equations; Structural models; DSGE models; Particle filter (search for similar items in EconPapers)
JEL-codes: C32 C36 E27 (search for similar items in EconPapers)
Date: 2017
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (14)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:econom:v:201:y:2017:i:2:p:198-211

DOI: 10.1016/j.jeconom.2017.08.003

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